This month I’m trying something a bit different with Boolean Explained. Getting great results still relies on a well-crafted query, of course, which is important here. But we’re going to look a bit more into the story telling, dashboards and widgets, to understand how a company might assess potential investment in influencers / brand ambassadors etc. through 2 use cases.

Use Case 1: What contribution has X, Y, Z influencer had on our brand / brand campaign?

Use Case 2: Using BW to conduct due diligence on influencers

Use Case 1:

The first thing to do is introduce the context of my example. Presenting, Liam Gallagher “BIBLICAL/CELESTIAL/MAJESTICAL/APPROACHABLE/HUMBLE/SPIRITUAL” rock n’ roll star and frontman of Oasis. Back in October 2024, he partnered with outdoor wear company Berghaus to launch a line of clothing. I chose this example because it is particularly interesting: Immediately following this launch, the likeness to the discount food store Lidl was observed and the Lidl very quickly jumped on this campaign as shown in the image below with a well placed billboard. As Berghaus, what can Brandwatch tell me about the campaign with a paid influencer, and a potential “crisis” aspect. 

Starting with a basic Share Of Voice: i.e. of all the conversation about my brand, how much has X, Y or Z influencer contributed. As Berghaus, you can begin to understand the contribution this campaign has had on your Share Of Voice in the market, as well as to what extent Lidl also impacted the campaign. 

In terms of structure, I have a Query for Berghaus, then 2 Tags for Liam Gallagher and Lidl, respectively. Here it is visualised in a Pie Chart:

If you want to be campaign specific, you could add a Tag with a single hashtag (or campaign specific keywords) in it with would be applied to both your campaign content, as well as content published by the influencer.  

Given Lidl were poking fun at the likeness, you might want to investigate the sentiment and/or emotions related to the conversation. After all, Berghaus is a quality outerwear retailer, whereas Lidl is a discount supermarket. This is how you break it down. To break it down further, simply change Queries to Sentiment and change Sentiment to Tags.

Use Case 2

It’s quite typical for contracts between brands and influencers, whether that be a sports star or media personality to include clauses that may allow the brand to terminate if their reputation is brought into disrepute, e.g. Adidas and “Ye”. This is one example of how being able to monitor sentiment, amongst other things.

For this example, I’ve used the 5 Kardashian sisters. If you’re considering working with an influencer / brand ambassador etc, here’s a great way to execute, in part, your due diligence. Or monitor sentiment and potential crisis whilst engaged with an influencer.

Firstly, you’ll want to set up your query. I asked Iris to help write mine then made some manual adjustments.

Next, your Categories.

No surprise, Kim is the most talked about Kardashian. 

What about Sentiment?

Disgust is more prominent with Kourtney. This could be a red flag.

You could then drill down into Kourtney’s sentiment and emotion to understand the % shift which is rather high.

As a brand considering their marketing and influencer strategy, you might ask: should we work with this influencer? 

There are some interesting dashboard components that might be able to help you make that decision. 

Starting with the Affinities component (find how out it works in our Help Center) which has identified that people who engage with Kourtney Kardashian are more likely to be into Metalworking, Kid Cudi, Logging… as well as Natural Hair and what looks like a reference to their astrological sign - Aquarius. 

There are a number of Demographic components (some are specific to X, for Example) to help you understand, based on your product or brand you want to market via the influencer, whether they would be able to reach the right people. 

Consider adding filters for specific sources based on what you might pay them to do / where they are most prominent in terms of publishing.

Top Subreddits could help you identify previously unknown sources of where the influencer is being discussed, continue to add filters around specific sources to build up a clearer picture of the conversation. 

Volume By Country is as it sounds, and will help you understand a global picture of conversation around an influencer (Note: geographic data isn’t available from all sources)

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